Research

Adaptive Charging Network Research Portal

ACN Dataset

The Adaptive Charging Network Dataset (ACN-Data) is a dynamic dataset of workplace EV charging
sessions. In this paper we describe the dataset, as well as some interesting user behavior it
exhibits. To demonstrate the usefulness of the dataset, we present three examples,
learning and predicting user behavior using Gaussian mixture models, optimally sizing on-site
solar generation for adaptive electric vehicle charging, and using workplace charging to smooth
the net demand Duck Curve.

ACN Simulator

The Adaptive Charging Network Simulator (ACN-Sim) is a data-driven,
open-source simulator based on our experience building and operating real-world
charging systems. This simulator provides researchers who may lack access to real
EV charging systems with a realistic environment to evaluate their algorithms and
test their assumptions. It also provides a common platform on which algorithms can
be evaluated head-to-head, allowing researchers to better understand and articulate
how their work fits into the existing literature.

Infrastructure and Algorithms

ACN Infrastructure and Algorithms

The Adaptive Charging Network (ACN) is a unique framework for large-scale smart EV charging. In
these papers we describe the hardware and software architecture of the ACN. We also present an
algorithm framework based on convex optimization and model predictive control which
can be used to schedule EV charging to achieve various objective such as maximizing energy
delivery in constrained infrastructure, reducing costs when subjected to time-varying prices, or
following demand response signals.

Optimality of Online LP

In its simplest form, optimal charging can be formulated as a linear program (LP) or a quadratic
program (QP). An offline LP assumes all future EV arrivals, departures, and energy demands are
known and computes the charging profiles of all EVs as an optimal solution of a single LP. An
online LP is an iterative algorithm in a model-predictive control fashion, and, in each
iteration, computes the charging profiles of existing EVs assuming there will not be any future
EV arrival. Offline LP is impractical but serves as a lower bound on the cost of online LP which
can be implemented in ACN. Extensive simulations using datasets from Caltech ACN and Google’s
charging facilities show that the performance of online LP is extremely close to that of offline
LP. We prove that, under appropriate assumptions, when online LP is feasible it indeed attains
offline optimal.

Smoothed Least-Laxity First Algorithm

In its simplest form, optimal charging can be formulated as a linear program (LP) or a quadratic
program (QP). An offline LP assumes all future EV arrivals, departures, and energy demands are
known and computes the charging profiles of all EVs as an optimal solution of a single LP. An
online LP is an iterative algorithm in a model-predictive control fashion, and, in each
iteration, computes the charging profiles of existing EVs assuming there will not be any future
EV arrival. Offline LP is impractical but serves as a lower bound on the cost of online LP which
can be implemented in ACN. Extensive simulations using datasets from Caltech ACN and Google’s
charging facilities show that the performance of online LP is extremely close to that of offline
LP. We prove that, under appropriate assumptions, when online LP is feasible it indeed attains
offline optimal.

Optimal Distributed EV Charging

We design a distributed iterative scheduling algorithm for EV charging where, in each iteration,
EVs update their charging profiles according to the control signal broadcast by an aggregator,
and the aggregator adjusts the control signal to guide their updates. The algorithm converges to
optimal charging profiles even when EVs can plug in at different times, update their charging
profiles at different times with different frequencies, and may use outdated control signals
when they update.